The present embodiments relate to valve modeling. The mitral valve (MV), located between the left atrium (LA) and the left ventricle (LV), controls the unidirectional blood flow from the LA towards the LV. The MV is a complex cardiac structure including two leaflets, the mitral annulus and tendineae chordae. The leaflets are attached to the left heart through the fibrous mitral annulus, whereas the other extremity, called free-edge, is tethered to the papillary muscles through the tendineae chordae. During diastole, the leaflets open as the blood enters the LV. When the myocardium starts to contract, the leaflets close to prevent the blood from going back to the atrium. Tendineae chordae tighten to ensure closure.
Mitral valve disease is one of the most common heart valve diseases, with a prevalence increasing with age. In mitral valve disease, blood flows back towards the LA during systole, so-called mitral regurgitation, decreasing cardiac efficiency. In severe cases, a surgical intervention may be necessary to repair, or even replace the incompetent valve. Suturing the two mitral leaflets together at the regurgitant hole may help patients with severe mitral insufficiency due to leaflet prolapse or calcified annulus. In the percutaneous edge-to-edge technique, the leaflets are attached using a clip delivered through a catheter. For these or other forms of clipping, careful preoperative planning is necessary to select respondent patients and to determine the clipping sites. Strict guidelines, such as the length of the leaflets and/or diameter of the valve opening, have been defined to qualify for clipping treatment. However, current selection guidelines still lack prediction power with respect to complications and effectiveness of the therapy in specific patients. It is not uncommon to perform several trials during the intervention and, in some cases, decide to place two clips (≈30% of the patients) or even to abort the procedure due to complications (≈10% of the patients).
The complexity of MV anatomy and its fast dynamics make accurate quantification of the MV anatomy from medical images difficult. Ultrasound images, such as three-dimensional over time (3D+t or 4D) transesophageal (TEE), may be used to evaluate MV function in patients. 3D+t TEE ultrasound not only shows the dynamics of the structure but also enables the clinicians to compute guideline parameters. Other modalities, such as DynaCT, may be used. However, MV quantification requires tedious and time-consuming expert delineation, with little computational assistance.
More automatic methods have been proposed to make MV assessment more efficient. An interactive algorithm based on thin-tissue detection and level-set deformable models may identify the MV and the LV endocardium in 3D TEE images. Detailed geometrical models may be obtained, but several user interactions are still necessary to guide the modeling. Alternatively, the succession of mitral annulus detection and tracking, leaflet segmentation of the open valve, and leaflet tracking using a deformable model that handles contacts and chordae stresses has been proposed. Temporal re-sampling of 3D+t TEE images acquired on multiple heartbeats may improve temporal consistency. However, it is not clear how that succession of steps generalizes on large populations, with wider spectrum of MV disease, because of the numerous parameters to set.
In another approach to modeling the MV anatomy, machine learning is used to detect the MV on 3D+t TEE, computed tomography (CT) or DynaCT images. Other heart valves and papillary tips may be detected. Adding biomechanical constraints may improve the robustness of MV tracking in the presence of noise and signal drop-off in medical images.
However, quantifying the current function of the MV might not be sufficient to plan the optimal treatment for a specific patient. Computational models of MV physiology have been proposed to study MV physiology and assess how the pathological MV dynamics may be modified after intervention. Three categories of computational MV models may be identified: structural models, fluid-structure interaction models and deformable models. However, current approaches may not be computed from clinical data of large populations due to idealized framework, require tedious manual process to build the anatomical models, and suffer from a lack of integrated system for streamlined, clinical applications.